Should a propensity score model be super? The utility of ensemble procedures for causal adjustment

Alam, Shomoita; Moodie, Erica E. M.; Stephens, David A.

doi:10.1002/sim.8075

Cited by 24 publications

(28 citation statements)

References 33 publications

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“…This issue may be related to the estimation of more extreme PSs values obtained with GBM, which could undermine the positivity assumption. These findings agree with the study of Alam and colleagues [ 82 ] who observed better covariate balance and lower bias when PS was estimated with LR or Super Learner than GBM.…”

Section: Discussionsupporting

confidence: 93%

Propensity Score Analysis with Partially Observed Baseline Covariates: A Practical Comparison of Methods for Handling Missing Data

Bottigliengo

Lorenzoni

Ocagli

et al. 2021

IJERPH

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(1) Background: Propensity score methods gained popularity in non-interventional clinical studies. As it may often occur in observational datasets, some values in baseline covariates are missing for some patients. The present study aims to compare the performances of popular statistical methods to deal with missing data in propensity score analysis. (2) Methods: Methods that account for missing data during the estimation process and methods based on the imputation of missing values, such as multiple imputations, were considered. The methods were applied on the dataset of an ongoing prospective registry for the treatment of unprotected left main coronary artery disease. The performances were assessed in terms of the overall balance of baseline covariates. (3) Results: Methods that explicitly deal with missing data were superior to classical complete case analysis. The best balance was observed when propensity scores were estimated with a method that accounts for missing data using a stochastic approximation of the expectation-maximization algorithm. (4) Conclusions: If missing at random mechanism is plausible, methods that use missing data to estimate propensity score or impute them should be preferred. Sensitivity analyses are encouraged to evaluate the implications methods used to handle missing data and estimate propensity score.

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Section: Discussionsupporting

confidence: 93%

Propensity Score Analysis with Partially Observed Baseline Covariates: A Practical Comparison of Methods for Handling Missing Data

Bottigliengo

Lorenzoni

Ocagli

et al. 2021

IJERPH

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“…Our method can also be adapted and extended to settings where different strategies for confounding adjustment, such as inverse probability weighting or matching, may be preferred. 21,22 Overall, this article introduces a flexible framework for incorporating observational data in prospective trial design, providing an empirical framework to support decision-making in pragmatic trials.…”

Section: Discussionmentioning

confidence: 99%

“…In small samples and when the overlap in the distribution of propensity scores is poor, a propensity score-adjusted regression model is preferable to matching, stratification, or weighting. [20][21][22]…”

Section: Confounding Adjustmentmentioning

confidence: 99%

Simulation-based design of pragmatic trials in psoriatic arthritis using propensity scores

et al. 2021

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Background/Aims: Design of clinical trials requires careful decision-making across several dimensions, including endpoints, eligibility criteria, and subgroup enrichment. Clinical trial simulation can be an informative tool in trial design, providing empirical evidence by which to evaluate and compare the results of hypothetical trials with varying designs. We introduce a novel simulation-based approach using observational data to inform the design of a future pragmatic trial. Methods: We utilize propensity score-adjusted models to simulate hypothetical trials under alternative endpoints and enrollment criteria. We apply our approach to the design of pragmatic trials in psoriatic arthritis, using observational data embedded within the Tight Control of Inflammation in Early Psoriatic Arthritis study to simulate hypothetical open-label trials comparing treatment with tumor necrosis factor-α inhibitors to methotrexate. We first validate our simulations of a trial with traditional enrollment criteria and endpoints against a recently published trial. Next, we compare simulated treatment effects in patient populations defined by traditional and broadened enrollment criteria, where the latter is consistent with a future pragmatic trial. In each trial, we also consider five candidate primary endpoints. Results: Our results highlight how changes in the enrolled population and primary endpoints may qualitatively alter study findings and the ability to detect heterogeneous treatment effects between clinical subgroups. For treatments of interest in the study of psoriatic arthritis, broadened enrollment criteria led to diluted estimated treatment effects. Endpoints with greater responsiveness to treatment compared with a traditionally used endpoint were identified. These considerations, among others, are important for designing a future pragmatic trial aimed at having high external validity with relevance for real-world clinical practice. Conclusion: Observational data may be leveraged to inform design decisions in pragmatic trials. Our approach may be generalized to the study of other conditions where existing trial data are limited or do not generalize well to real-world clinical practice, but where observational data are available.

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“…For this reason, automatic variable selection approaches (eg, stepwise) or prediction-based measures of fit (eg, C-statistic), which seek best prediction of treatment allocation when specifying the PS model, may not provide the best balance for the confounders and favor variables that are strongly predictive of the treatment, even if they are only weakly or not at all predictive of the outcome. 22…”

Section: Initial Data Summary and The Propensity Scorementioning

confidence: 99%

Formulating causal questions and principled statistical answers

Goetghebeur

Cessie

Stavola

et al. 2020

Statistics in Medicine

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Although review papers on causal inference methods are now available, there is a lack of introductory overviews on what they can render and on the guiding criteria for choosing one particular method. This tutorial gives an overview in situations where an exposure of interest is set at a chosen baseline (“point exposure”) and the target outcome arises at a later time point. We first phrase relevant causal questions and make a case for being specific about the possible exposure levels involved and the populations for which the question is relevant. Using the potential outcomes framework, we describe principled definitions of causal effects and of estimation approaches classified according to whether they invoke the no unmeasured confounding assumption (including outcome regression and propensity score‐based methods) or an instrumental variable with added assumptions. We mainly focus on continuous outcomes and causal average treatment effects. We discuss interpretation, challenges, and potential pitfalls and illustrate application using a “simulation learner,” that mimics the effect of various breastfeeding interventions on a child's later development. This involves a typical simulation component with generated exposure, covariate, and outcome data inspired by a randomized intervention study. The simulation learner further generates various (linked) exposure types with a set of possible values per observation unit, from which observed as well as potential outcome data are generated. It thus provides true values of several causal effects. R code for data generation and analysis is available on http://www.ofcaus.org, where SAS and Stata code for analysis is also provided.

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Should a propensity score model be super? The utility of ensemble procedures for causal adjustment

Cited by 24 publications

References 33 publications

Propensity Score Analysis with Partially Observed Baseline Covariates: A Practical Comparison of Methods for Handling Missing Data

Propensity Score Analysis with Partially Observed Baseline Covariates: A Practical Comparison of Methods for Handling Missing Data

Simulation-based design of pragmatic trials in psoriatic arthritis using propensity scores

Formulating causal questions and principled statistical answers

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